(Last updated Feb. 11, 2015.)

What could an economics graduate student do to improve our strategic picture of superintelligence? What about a computer science professor? A policy analyst at RAND? A program director at IARPA?

In the last chapter of Superintelligence, Nick Bostrom writes:

We find ourselves in a thicket of strategic complexity and surrounded by a dense mist of uncertainty. Though many considerations have been discerned, their details and interrelationships remain unclear and iffy — and there might be other factors we have not thought of yet. How should we act in this predicament?

… Against a backdrop of perplexity and uncertainty, [strategic] analysis stands out as being of particularly high expected value. Illumination of our strategic situation would help us target subsequent interventions more effectively. Strategic analysis is especially needful when we are radically uncertain not just about some detail of some peripheral matter but about the cardinal qualities of the central things. For many key parameters, we are radically uncertain even about their sign…

The hunt for crucial considerations… will often require crisscrossing the boundaries between different academic disciplines and other fields of knowledge.

Bostrom does not, however, provide a list of specific research projects that could illuminate our strategic situation and thereby “help us target subsequent interventions more effectively.”

Below is my personal list of studies which could illuminate our strategic situation with regard to superintelligence. I’m hosting it on my personal site rather than MIRI’s blog to make it clear that this is not “MIRI’s official list of project ideas.” Other researchers at MIRI would, I’m sure, put together a different list. [Read more…]

When you’re not sure what to think about something, or what to do in a certain situation, do you instinctively turn to a successful domain expert, or to someone you know who seems generally very smart?

I think most people don’t respect individual differences in intelligence and rationality enough. But some people in my local community tend to exhibit the opposite failure mode. They put too much weight on a person’s signals of explicit rationality (“Are they Bayesian?”), and place too little weight on domain expertise (and the domain-specific tacit rationality that often comes with it).

This comes up pretty often during my work for MIRI. We’re considering how to communicate effectively with academics, or how to win grants, or how to build a team of researchers, and some people (not necessarily MIRI staff) will tend to lean heavily on the opinions of the most generally smart people they know, even though those smart people have no demonstrated expertise or success on the issue being considered. In contrast, I usually collect the opinions of some smart people I know, and then mostly just do what people with a long track record of success on the issue say to do. And that dumb heuristic seems to work pretty well.

Yes, there are nuanced judgment calls I have to make about who has expertise on what, exactly, and whether MIRI’s situation is sufficiently analogous for the expert’s advice to work at MIRI. And I must be careful to distinguish credentials-expertise from success-expertise (aka RSPRT-expertise). And this process doesn’t work for decisions on which there are no success-experts, like long-term AI forecasting. But I think it’s easier for smart people to overestimate their ability to model problems outside their domains of expertise, and easier to underestimate all the subtle things domain experts know, than vice-versa.

Yudkowsky writes:

In general and across all instances I can think of so far, I do not agree with the part of your futurological forecast in which you reason, “After event W happens, everyone will see the truth of proposition X, leading them to endorse Y and agree with me about policy decision Z.”

…

Example 2: “As AI gets more sophisticated, everyone will realize that real AI is on the way and then they’ll start taking Friendly AI development seriously.”

Alternative projection: As AI gets more sophisticated, the rest of society can’t see any difference between the latest breakthrough reported in a press release and that business earlier with Watson beating Ken Jennings or Deep Blue beating Kasparov; it seems like the same sort of press release to them. The same people who were talking about robot overlords earlier continue to talk about robot overlords. The same people who were talking about human irreproducibility continue to talk about human specialness. Concern is expressed over technological unemployment the same as today or Keynes in 1930, and this is used to fuel someone’s previous ideological commitment to a basic income guarantee, inequality reduction, or whatever. The same tiny segment of unusually consequentialist people are concerned about Friendly AI as before. If anyone in the science community does start thinking that superintelligent AI is on the way, they exhibit the same distribution of performance as modern scientists who think it’s on the way, e.g. Hugo de Garis, Ben Goertzel, etc.

My own projection goes more like this:

As AI gets more sophisticated, and as more prestigious AI scientists begin to publicly acknowledge that AI is plausibly only 2-6 decades away, policy-makers and research funders will begin to respond to the AGI safety challenge, just like they began to respond to CFC damages in the late 70s, to global warming in the late 80s, and to synbio developments in the 2010s. As for society at large, I dunno. They’ll think all kinds of random stuff for random reasons, and in some cases this will seriously impede effective policy, as it does in the USA for science education and immigration reform. Because AGI lends itself to arms races and is harder to handle adequately than global warming or nuclear security are, policy-makers and industry leaders will generally know AGI is coming but be unable to fund the needed efforts and coordinate effectively enough to ensure good outcomes.

At least one clear difference between my projection and Yudkowsky’s is that I expect AI-expert performance on the problem to improve substantially as a greater fraction of elite AI scientists begin to think about the issue in Near mode rather than Far mode.

As a friend of mine suggested recently, current elite awareness of the AGI safety challenge is roughly where elite awareness of the global warming challenge was in the early 80s. Except, I expect elite acknowledgement of the AGI safety challenge to spread more slowly than it did for global warming or nuclear security, because AGI is tougher to forecast in general, and involves trickier philosophical nuances. (Nobody was ever tempted to say, “But as the nuclear chain reaction grows in power, it will necessarily become more moral!”)

Still, there is a worryingly non-negligible chance that AGI explodes “out of nowhere.” Sometimes important theorems are proved suddenly after decades of failed attempts by other mathematicians, and sometimes a computational procedure is sped up by 20 orders of magnitude with a single breakthrough.

What does MIRI’s research program study?

The most established term for this was coined by MIRI founder Eliezer Yudkowsky: “Friendly AI.” The term has some advantages, but it might suggest that MIRI is trying to build C-3PO, and it sounds a bit whimsical for a serious research program.

What about safe AGI or AGI safety? These terms are probably easier to interpret than Friendly AI. Also, people like being safe, and governments like saying they’re funding initiatives to keep the public safe.

A friend of mine worries that these terms could provoke a defensive response (in AI researchers) of “Oh, so you think me and everybody else in AI is working on unsafe AI?” But I’ve never actually heard that response to “AGI safety” in the wild, and AI safety researchers regularly discuss “software system safety” and “AI safety” and “agent safety” and more specific topics like “safe reinforcement learning” without provoking negative reactions from people doing regular AI research.

I’m more worried that a term like “safe AGI” could provoke a response of “So you’re trying to make sure that a system which is smarter than humans, and able to operate in arbitrary real-world environments, and able to invent new technologies to achieve its goals, will be safe? Let me save you some time and tell you right now that’s impossible. Your research program is a pipe dream.”

My reply goes something like “Yeah, it’s way beyond our current capabilities, but lots of things that once looked impossible are now feasible because people worked really hard on them for a long time, and we don’t think we can get the whole world to promise never to build AGI just because it’s hard to make safe, so we’re going to give AGI safety a solid try for a few decades and see what can be discovered.” But that’s probably not all that reassuring. [Read more…]